code: def test_reduce_where(
      self, func, nin, nout, identity, shape, axis, dtype
  ):
      if (nin, nout) != (2, 1):
          self.skipTest(
              f"reduce requires (nin, nout)=(2, 1); got {(nin, nout)=}"
          )
  
      # Need initial if identity is None
      initial = 1 if identity is None else None
  
      def jnp_fun(arr, where):
          return jnp.frompyfunc(
              func, nin, nout, identity=identity
          ).reduce(arr, where=where, axis=axis, initial=initial)
  
      @cast_outputs
      def np_fun(arr, where):
          # Workaround for https://github.com/numpy/numpy/issues/24530
          # TODO(jakevdp): remove this when possible.
          initial_workaround = identity if initial is None else initial
          return np.frompyfunc(
              func, nin=nin, nout=nout, identity=identity
          ).reduce(
              arr, where=where, axis=axis, initial=initial_workaround
          )
  
      rng = jtu.rand_default(self.rng())
      rng_where = jtu.rand_bool(self.rng())
      args_maker = lambda: [rng(shape, dtype), rng_where(shape, bool)]
  
      self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
      self._CompileAndCheck(jnp_fun, args_maker)
  
imports: from functools import partial
  from absl.testing import absltest
  import numpy
  import jax
  import jax.numpy
  from jax._src import test_util
  from jax._src.numpy.ufunc_api import get_if_single_primitive
  from jax import config
label_desc: Use image node_id 7 for calling the LaxNumpyUfuncTests obj's underlying member method code with example usage: obj.test_reduce_where(func, nin, nout, identity, shape, axis, dtype) and returns: jnp, np